An autocalibrating post-Cartesian parallel imaging
method is presented. It is based on structured, low-rank matrix completion
which is an extension of compressed sensing to Matrices. The method does not
require a fully sampled autocalibration area in k-space. Instead it jointly
calibrates and reconstructs the signal from the undersampled data alone.
Results using spiral sampling are demonstrated showing similarly good
reconstruction compared to method that use explicit calibration data.